rail.interactive.estimation.algos.flexzboost module

rail.interactive.estimation.algos.flexzboost.flex_z_boost_estimator(**kwargs)

FlexZBoost-based CatEstimator

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The main interface method for the photo-z estimation

This will attach the input data (defined in inputs as “input”) to this Estimator (for introspection and provenance tracking). Then call the run(), validate(), and finalize() methods.

The run method will call _process_chunk(), which needs to be implemented in the subclass, to process input data in batches. See RandomGaussEstimator for a simple example.

Finally, this will return a QPHandle for access to that output data.

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This function was generated from the function rail.estimation.algos.flexzboost.FlexZBoostEstimator.estimate

Parameters:

  • input_data (TableLike, required) – A dictionary of all input data

  • model (numpy.ndarray, required)

  • chunk_size (int, optional) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing Default: 10000

  • hdf5_groupname (str, optional) – name of hdf5 group for data, if None, then set to ‘’ Default: photometry

  • zmin (float, optional) – The minimum redshift of the z grid or sample Default: 0.0

  • zmax (float, optional) – The maximum redshift of the z grid or sample Default: 3.0

  • nzbins (int, optional) – The number of gridpoints in the z grid Default: 301

  • id_col (str, optional) – name of the object ID column Default: object_id

  • redshift_col (str, optional) – name of redshift column Default: redshift

  • calc_summary_stats (bool, optional) – Compute summary statistics Default: False

  • calculated_point_estimates (list, optional) – List of strings defining which point estimates to automatically calculate using qp.Ensemble.Options include, ‘mean’, ‘mode’, ‘median’. Default: []

  • recompute_point_estimates (bool, optional) – Force recomputation of point estimates Default: False

  • nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

  • mag_limits (dict, optional) – Limiting magnitudes by filter Default: {‘mag_u_lsst’: 27.79, ‘mag_g_lsst’: 29.04, ‘mag_r_lsst’: 29.06,…}

  • bands (list, optional) – Names of columns for magnitude by filter band Default: [‘mag_u_lsst’, ‘mag_g_lsst’, ‘mag_r_lsst’, ‘mag_i_lsst’,…]

  • err_bands (list, optional) – Names of columns for magnitude errors by filter band Default: [‘mag_err_u_lsst’, ‘mag_err_g_lsst’, ‘mag_err_r_lsst’,…]

  • ref_band (str, optional) – band to use in addition to colors Default: mag_i_lsst

  • qp_representation (str, optional) – qp generator to use. [interp|flexzboost] Default: interp

  • include_mag_err (bool, optional) – Include magnitude error in the training and estimationprocess Default: False

Returns:

Handle providing access to QP ensemble with output data

Return type:

qp.core.ensemble.Ensemble

rail.interactive.estimation.algos.flexzboost.flex_z_boost_informer(**kwargs)

Train a FlexZBoost CatInformer

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The main interface method for Informers

This will attach the input_data to this Informer (for introspection and provenance tracking).

Then it will call the run(), validate() and finalize() methods, which need to be implemented by the sub-classes.

The run() method will need to register the model that it creates to this Estimator by using self.add_data(‘model’, model).

Finally, this will return a ModelHandle providing access to the trained model.

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This function was generated from the function rail.estimation.algos.flexzboost.FlexZBoostInformer.inform

Parameters:

  • training_data (TableLike, required) – dictionary of all input data, or a TableHandle providing access to it

  • hdf5_groupname (str, optional) – name of hdf5 group for data, if None, then set to ‘’ Default: photometry

  • zmin (float, optional) – The minimum redshift of the z grid or sample Default: 0.0

  • zmax (float, optional) – The maximum redshift of the z grid or sample Default: 3.0

  • nzbins (int, optional) – The number of gridpoints in the z grid Default: 301

  • nondetect_val (float, optional) – value to be replaced with magnitude limit for non detects Default: 99.0

  • mag_limits (dict, optional) – Limiting magnitudes by filter Default: {‘mag_u_lsst’: 27.79, ‘mag_g_lsst’: 29.04, ‘mag_r_lsst’: 29.06,…}

  • bands (list, optional) – Names of columns for magnitude by filter band Default: [‘mag_u_lsst’, ‘mag_g_lsst’, ‘mag_r_lsst’, ‘mag_i_lsst’,…]

  • err_bands (list, optional) – Names of columns for magnitude errors by filter band Default: [‘mag_err_u_lsst’, ‘mag_err_g_lsst’, ‘mag_err_r_lsst’,…]

  • ref_band (str, optional) – band to use in addition to colors Default: mag_i_lsst

  • redshift_col (str, optional) – name of redshift column Default: redshift

  • retrain_full (bool, optional) – if True, re-run the fit with the full training set, including data set aside for bump/sharpen validation. If False, only use the subset defined via trainfrac fraction Default: True

  • trainfrac (float, optional) – fraction of training data to use for training (rest used for bump thresh and sharpening determination) Default: 0.75

  • seed (int, optional) – Random number seed Default: 1138

  • bumpmin (float, optional) – minimum value in grid of thresholds checked to optimize removal of spurious small bumps Default: 0.02

  • bumpmax (float, optional) – max value in grid checked for removal of small bumps Default: 0.35

  • nbump (int, optional) – number of grid points in bumpthresh grid search Default: 20

  • sharpmin (float, optional) – min value in grid checked in optimal sharpening parameter fit Default: 0.7

  • sharpmax (float, optional) – max value in grid checked in optimal sharpening parameter fit Default: 2.1

  • nsharp (int, optional) – number of search points in sharpening fit Default: 15

  • max_basis (int, optional) – maximum number of basis funcitons to use in density estimate Default: 35

  • basis_system (str, optional) – type of basis sytem to use with flexcode Default: cosine

  • regression_params (dict, optional) – dictionary of options passed to flexcode, includes max_depth (int), and objective, which should be set to reg:squarederror Default: {‘max_depth’: 8, ‘objective’: ‘reg:squarederror’}

  • include_mag_err (bool, optional) – Include magnitude error in the training and estimationprocess Default: False

Returns:

Handle providing access to trained model

Return type:

numpy.ndarray